# Load packages
Warning messages:
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library(ggplot2)
library(dplyr)
library(tidyr)
library(tidyverse)
library(plotly)
str(coronavirus)
tibble [157,000 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ ID : int [1:157000] 1 2 3 4 5 6 7 8 9 10 ...
$ date : Date[1:157000], format: "2020-01-22" "2020-01-23" ...
$ province: logi [1:157000] NA NA NA NA NA NA ...
$ country : chr [1:157000] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
$ lat : num [1:157000] 33.9 33.9 33.9 33.9 33.9 ...
$ long : num [1:157000] 67.7 67.7 67.7 67.7 67.7 ...
$ type : chr [1:157000] "confirmed" "confirmed" "confirmed" "confirmed" ...
$ cases : num [1:157000] 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "problems")= tibble [45,800 × 5] (S3: tbl_df/tbl/data.frame)
..$ row : int [1:45800] 37001 37002 37003 37004 37005 37006 37007 37008 37009 37010 ...
..$ col : chr [1:45800] "province" "province" "province" "province" ...
..$ expected: chr [1:45800] "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" ...
..$ actual : chr [1:45800] "Alberta" "Alberta" "Alberta" "Alberta" ...
..$ file : chr [1:45800] "'coronavirus.csv'" "'coronavirus.csv'" "'coronavirus.csv'" "'coronavirus.csv'" ...
- attr(*, "spec")=
.. cols(
.. date = col_date(format = ""),
.. province = col_logical(),
.. country = col_character(),
.. lat = col_double(),
.. long = col_double(),
.. type = col_character(),
.. cases = col_double()
.. )
Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
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coronavirus %>%
filter(date == max(date)) %>%
select(country, type, cases) %>%
group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total_cases) %>%
arrange(-confirmed)
`summarise()` regrouping output by 'country' (override with `.groups` argument)
coronavirus %>%
group_by(type, date) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type, values_from = total_cases) %>%
arrange(date) %>%
mutate(active = confirmed - death - recovered) %>%
mutate(active_total = cumsum(active),
recovered_total = cumsum(recovered),
death_total = cumsum(death)) %>%
plot_ly(x = ~ date,
y = ~ active_total,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace(y = ~ death_total,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(y = ~recovered_total,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Distribution of Covid19 Cases Worldwide",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Johns Hopkins University Center for Systems Science and Engineering"))
`summarise()` regrouping output by 'type' (override with `.groups` argument)
conf_df <- coronavirus %>%
filter(type == "confirmed") %>%
group_by(country) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases) %>%
mutate(parents = "Confirmed") %>%
ungroup()
`summarise()` ungrouping output (override with `.groups` argument)
plot_ly(data = conf_df,
type= "treemap",
values = ~total_cases,
labels= ~ country,
parents= ~parents,
domain = list(column=0),
name = "Confirmed",
textinfo="label+value+percent parent")
ggplot(confirmed_cases, aes(x = date, y = cum_cases)) +
geom_line(aes(x = date, y = cum_cases)) +
ylab("Cumulative confirmed cases")

head(coronavirus)
coronavirus = tibble::rowid_to_column(coronavirus, "ID")
head(coronavirus)
# coronavirus = coronavirus %>%
# group_by(ID) %>%
# mutate(cum_cases = cumsum(cases))
# coronavirus %>% head(70)
mutate(group_by(coronavirus, ID), cumcases = cumsum(cases))
df <- data.frame(id = rep(1:3, each = 5),
hour = rep(1:5, 3),
value = sample(1:15))
mutate(group_by(df,id), cumsum=cumsum(value))
plt_cum_confirmed_cases_china <- ggplot(confirmed_cases_china, aes(date, cumsum)) +
geom_line() +
ylab("Cumulative confirmed cases")
# See the plot
plt_cum_confirmed_cases_china

who_events <- tribble(
~ date, ~ event,
"2020-01-30", "Global health emergency declared",
"2020-03-11", "Pandemic declared",
"2020-02-13", "China reporting change"
) %>%
mutate(date = as.Date(date))
# Using who_events, add vertical dashed lines with an xintercept at date
# and text at date, labeled by event, and at 100000 on the y-axis
plt_cum_confirmed_cases_china +
geom_vline(aes(xintercept = date), data = who_events, linetype = "dashed") +
geom_text(aes(x = date, label = event), data = who_events, y = 1e5)

# Filter for China, from Feb 15
china_after_feb15 <- confirmed_cases_china %>%
filter(date >= "2020-02-15")
# Using china_after_feb15, draw a line plot cum_cases vs. date
# Add a smooth trend line using linear regression, no error bars
ggplot(china_after_feb15, aes(date, cumsum)) +
geom_line() +
geom_smooth(method = "lm", formula = 'y ~ x', se = FALSE) +
ylab("Cumulative confirmed cases")

not_china = coronavirus %>%
group_by(date) %>%
filter(country != "China", type == "confirmed")%>%
summarize(cases = sum(cases)) %>%
mutate(cumsum = cumsum(cases))
`summarise()` ungrouping output (override with `.groups` argument)
not_china
# not_china %>%
# group_by(date) %>%
# summarize(cases = sum(cases)) %>%
# select(date, country, cases, cumsum)
glimpse(not_china)
Rows: 200
Columns: 3
$ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01-26, 2020-01-27…
$ cases <dbl> 7, 4, 10, 7, 15, 7, 19, 10, 14, 32, 22, 10, 14, 20, 12, 12, 70, 30, 15,…
$ cumsum <dbl> 7, 11, 21, 28, 43, 50, 69, 79, 93, 125, 147, 157, 171, 191, 203, 215, 2…
not_china2 = coronavirus %>%
group_by(date) %>%
filter(country != "China", type == "confirmed")%>%
summarize(cases = sum(cases)) %>%
mutate(cumsum = cumsum(cases))
`summarise()` ungrouping output (override with `.groups` argument)
not_china2
world_after_feb15 <- all_countries %>%
filter(date >= "2020-02-15")
all_countries_trend_lin <- ggplot(world_after_feb15, aes(x = date, y =cumsum)) +
geom_line() +
geom_smooth(method = "lm", formula = 'y ~ x', se = FALSE) +
ylab("Cumulative confirmed cases")
# See the result
all_countries_trend_lin

# Using not_china, draw a line plot cum_cases vs. date
# Add a smooth trend line using linear regression, no error bars
plt_not_china_trend_lin <- ggplot(not_china, aes(date, cumsum)) +
geom_line() +
geom_smooth(method = "lm", formula = 'y ~ x', se = FALSE) +
ylab("Cumulative confirmed cases")
# See the result
plt_not_china_trend_lin

plt_not_china_trend_lin +
scale_y_log10()

Filter by top 7 countries
target = c("Brazil", "India", "Mexico", "Peru", "Russia", "South Africa", "US")
top_7countries = bycountry %>%
filter(country %in% target)
top_7countries
NA
ggplot(top_7countries, aes(date, total_cases)) +
There were 14 warnings (use warnings() to see them)
geom_line(aes(group = country, color = country))+
ylab("Cumulative confirmed cases")

---
title: "R Notebook"
output: html_notebook
---


```{r}
# Load packages
library(ggplot2)
library(dplyr)
library(tidyr)
library(tidyverse)
library(plotly)

```

```{r}
# Read datasets/confirmed_cases_worldwide.csv into confirmed_cases_worldwide
coronavirus <- read_csv("coronavirus.csv")

head(coronavirus)
str(coronavirus)

# # See the result
bycountry <- coronavirus %>% 
  filter(type == "confirmed") %>%
  group_by(country) %>%
  summarise(total_cases = sum(cases)) %>%
  arrange(-total_cases)

bycountry


confirmed_cases = coronavirus %>% 
  group_by(date) %>%
  filter(type == "confirmed") %>%
  summarize(cases = sum(cases)) %>%
  mutate(cumsum = cumsum(cases))

# confirmed_cases_china = coronavirus %>%
#   group_by(date) %>%
#   filter(country == "China", type == "confirmed")%>% 
#   summarize(cases = sum(cases)) %>%
#   mutate(cumsum = cumsum(cases))
# confirmed_cases_china



(confirmed_cases)





```


```{r}
coronavirus %>% 
  filter(date == max(date)) %>%
  select(country, type, cases) %>%
  group_by(country, type) %>%
  summarise(total_cases = sum(cases)) %>%
  pivot_wider(names_from = type,
              values_from = total_cases) %>%
  arrange(-confirmed)
```


```{r}
coronavirus %>% 
  group_by(type, date) %>%
  summarise(total_cases = sum(cases)) %>%
  pivot_wider(names_from = type, values_from = total_cases) %>%
  arrange(date) %>%
  mutate(active = confirmed - death - recovered) %>%
  mutate(active_total = cumsum(active),
                recovered_total = cumsum(recovered),
                death_total = cumsum(death)) %>%
  plot_ly(x = ~ date,
                  y = ~ active_total,
                  name = 'Active', 
                  fillcolor = '#1f77b4',
                  type = 'scatter',
                  mode = 'none', 
                  stackgroup = 'one') %>%
  add_trace(y = ~ death_total, 
             name = "Death",
             fillcolor = '#E41317') %>%
  add_trace(y = ~recovered_total, 
            name = 'Recovered', 
            fillcolor = 'forestgreen') %>%
  layout(title = "Distribution of Covid19 Cases Worldwide",
         legend = list(x = 0.1, y = 0.9),
         yaxis = list(title = "Number of Cases"),
         xaxis = list(title = "Source: Johns Hopkins University Center for Systems Science and Engineering"))
```

```{r}
conf_df <- coronavirus %>% 
  filter(type == "confirmed") %>%
  group_by(country) %>%
  summarise(total_cases = sum(cases)) %>%
  arrange(-total_cases) %>%
  mutate(parents = "Confirmed") %>%
  ungroup() 
  
  plot_ly(data = conf_df,
          type= "treemap",
          values = ~total_cases,
          labels= ~ country,
          parents=  ~parents,
          domain = list(column=0),
          name = "Confirmed",
          textinfo="label+value+percent parent")
```

```{r}
ggplot(confirmed_cases, aes(x = date, y = cum_cases)) +
  geom_line(aes(x = date, y = cum_cases)) +
  ylab("Cumulative confirmed cases")
```

```{r}
head(coronavirus)
```
```{r}
coronavirus = tibble::rowid_to_column(coronavirus, "ID")
head(coronavirus)
```

```{r}
# coronavirus = coronavirus %>%
#   group_by(ID) %>% 
#   mutate(cum_cases = cumsum(cases))
# coronavirus %>% head(70)

```


```{r}
mutate(group_by(coronavirus, ID), cumsum = cumsum(cases))
```


```{r}
# df <- data.frame(id = rep(1:3, each = 5),
#                  hour = rep(1:5, 3),
#                  value = sample(1:15))
# 
# mutate(group_by(df,id), cumsum=cumsum(value))
```

```{r}
confirmed_cases_china = coronavirus %>%
  group_by(date) %>%
  filter(country == "China", type == "confirmed")%>% 
  summarize(cases = sum(cases)) %>%
  mutate(cumsum = cumsum(cases))
confirmed_cases_china

confirmed_cases_china %>% 
  group_by(date) %>% 
  summarize(cases = sum(cases)) %>% 
  select(date, country, cases, cumsum)
  

glimpse(confirmed_cases_china)
```

```{r}
plt_cum_confirmed_cases_china <- ggplot(confirmed_cases_china, aes(date, cumsum)) +
  geom_line() +
  ylab("Cumulative confirmed cases") 

# See the plot
plt_cum_confirmed_cases_china
```

```{r}
who_events <- tribble(
  ~ date, ~ event,
  "2020-01-30", "Global health emergency declared",
  "2020-03-11", "Pandemic declared",
  "2020-02-13", "China reporting change"
) %>%
  mutate(date = as.Date(date))

# Using who_events, add vertical dashed lines with an xintercept at date
# and text at date, labeled by event, and at 100000 on the y-axis
plt_cum_confirmed_cases_china + 
  geom_vline(aes(xintercept = date), data = who_events, linetype = "dashed") +
  geom_text(aes(x = date, label = event), data = who_events, y = 1e5)

```

```{r}
# Filter for China, from Feb 15
china_after_feb15 <- confirmed_cases_china %>%
  filter(date >= "2020-02-15")

# Using china_after_feb15, draw a line plot cum_cases vs. date
# Add a smooth trend line using linear regression, no error bars
ggplot(china_after_feb15, aes(date, cumsum)) +
  geom_line() +
  geom_smooth(method = "lm", formula = 'y ~ x', se = FALSE) +
  ylab("Cumulative confirmed cases")
```

```{r}
not_china = coronavirus %>%
  group_by(date) %>%
  filter(country != "China", type == "confirmed")%>% 
  summarize(cases = sum(cases)) %>%
  mutate(cumsum = cumsum(cases))
not_china

# not_china %>% 
#   group_by(date) %>% 
#   summarize(cases = sum(cases)) %>% 
#   select(date, country, cases, cumsum)


glimpse(not_china)
```

```{r}
not_china2 = coronavirus %>%
  group_by(date) %>%
  filter(country != "China", type == "confirmed")%>% 
  summarize(cases = sum(cases)) %>%
  mutate(cumsum = cumsum(cases))
not_china2
```

```{r}

world_after_feb15 <- all_countries %>%
  filter(date >= "2020-02-15")


all_countries_trend_lin <- ggplot(world_after_feb15, aes(x = date, y =cumsum)) +
  geom_line() +
  geom_smooth(method = "lm", formula = 'y ~ x', se = FALSE) +
  ylab("Cumulative confirmed cases")


# See the result
all_countries_trend_lin 

```



```{r}
# Using not_china, draw a line plot cum_cases vs. date
# Add a smooth trend line using linear regression, no error bars
plt_not_china_trend_lin <- ggplot(not_china, aes(date, cumsum)) +
  geom_line() +
  geom_smooth(method = "lm", formula = 'y ~ x', se = FALSE) +
  ylab("Cumulative confirmed cases")

# See the result
plt_not_china_trend_lin 
```

```{r}
plt_not_china_trend_lin + 
  scale_y_log10()
```

```{r}
# Group by country, summarize to calculate total cases, find the top 7
bycountry <- coronavirus %>% 
  filter(type == "confirmed") %>%
  group_by(country, date) %>%
  summarise(total_cases = sum(cases)) %>%
  arrange(-total_cases)


bycountry



top_countries_by_total_cases = bycountry %>% 
  group_by(country) %>%
  top_n(7, total_cases)




top_countries_by_total_cases <- bycountry %>%
  group_by(country) %>%
  summarize(total_cases = sum(total_cases)) %>%
  top_n(7, total_cases)



# See the result
top_countries_by_total_cases

```

Filter by top 7 countries
```{r}
target = c("Brazil", "India", "Mexico", "Peru", "Russia", "South Africa", "US")
top_7countries = bycountry %>% 
  filter(country %in% target)

top_7countries

```


```{r}
ggplot(top_7countries, aes(date, total_cases))  +
    geom_line(aes(group = country, color = country))+
    ylab("Cumulative confirmed cases")
```







